bench_plot_svd.py 文件源码

def compute_bench(samples_range, features_range, n_iter=3, rank=50):

    it = 0

    results = defaultdict(lambda: [])

    max_it = len(samples_range) * len(features_range)
    for n_samples in samples_range:
        for n_features in features_range:
            it += 1
            print('====================')
            print('Iteration %03d of %03d' % (it, max_it))
            print('====================')
            X = make_low_rank_matrix(n_samples, n_features,
                                  effective_rank=rank,
                                  tail_strength=0.2)

            gc.collect()
            print("benchmarking scipy svd: ")
            tstart = time()
            svd(X, full_matrices=False)
            results['scipy svd'].append(time() - tstart)

            gc.collect()
            print("benchmarking scikit-learn randomized_svd: n_iter=0")
            tstart = time()
            randomized_svd(X, rank, n_iter=0)
            results['scikit-learn randomized_svd (n_iter=0)'].append(
                time() - tstart)

            gc.collect()
            print("benchmarking scikit-learn randomized_svd: n_iter=%d "
                  % n_iter)
            tstart = time()
            randomized_svd(X, rank, n_iter=n_iter)
            results['scikit-learn randomized_svd (n_iter=%d)'
                    % n_iter].append(time() - tstart)

    return results